Quantitative Plant Biology

Mechanistic models of plant regulatory networks typically require extensive parameterization, limiting their generalisation and scalability. Here we present a parameter-free, topology-driven model of shoot branching that predicts phenotypic outcomes from network structure alone. We constructed a signed, directed causal network by distilling regulatory relationships from the published literature spanning many laboratories, species, years, data types, and methodological frameworks. This extracted the essential logic of the system, consistent with developmental-biological reasoning and anchored in empirical evidence. Using PSoup, which automatically translates network topology into algebraic equations, the model propagates information across the network and predicts the qualitative direction of change relative to a defined baseline, mirroring the comparative framework of biological experiments. The pipeline, from network construction through automated equation generation to prediction, is transparent and reproducible. Trained against branching phenotype data with 78 diverse perturbations spanning genetic mutations and hormone treatments, the model achieved 86% accuracy in predicting branching direction. On an independent test set of 84 perturbations measuring bud release and gene expression at nodes not used during training, accuracy reached 75%. The approach highlighted deficiencies in our understanding of the topology of the network around SMXL 6/7/8 and ABA nodes. Other errors came mainly from modelling choices, such as the threshold for scoring a node as changed relative to baseline. Beyond shoot branching, this work demonstrates a general strategy for synthesizing biological knowledge into validated predictive networks, providing a foundation for both applied breeding and the advancement of fundamental biology.

Leaf reflectance spectra are emerging as a viable substitute for gas-exchange measurements of photosynthetic capacity, with a community benchmark reporting that a spectrum accurately recovers most Farquhar-von Caemmerer-Berry (FvCB) parameters. This study re-scores the recovery under dataset-blocked, species-blocked, and leave-one-dataset-out designs, measuring the split-half reliability of each curated parameter. We constructed a convolutional encoder that maps a spectrum to the four parameters through a fixed, differentiable FvCB decoder trained on measured assimilation. A conspecific of 97.4% of held-out leaves were present in the training set, and accuracy is lost along the dataset axis but not along the species axis. Under blocked evaluation, a spectrum constrains a single capacity axis. Jmax25 retains only 17% of its recovery when Vcmax25 is held constant, and the Jmax25:Vcmax25 ratio is not predicted above a median null. The curated values of TPU25 are not reproducible, whereas those of Rday25 are well determined, but its recovery fails due to the loss. The published study measures interpolation rather than transfer, and spectra constrain less of the FvCB parameter space than assumed, including the carboxylation to electron transport balance. Routing predictions through explicit biochemistry makes identifiability measurable, although it does not improve prediction accuracy.

Carbon allocation governs how plants partition assimilates among leaves, roots, stems, and reproductive organs, directly shaping growth and yield. Most crop models represent this partitioning using fixed or empirically derived coefficients, which limits their ability to predict how allocation responds to genotype, environment, or their interaction. Eco-evolutionary optimality theory offers an alternative where the allocation strategy is allowed to emerge from the marginal costs and benefits of investing carbon in each organ. While this approach has been applied successfully to trees, it has not previously been developed for herbaceous plants. Here, we present DAESIM2-Plant, a mechanistic plant growth model that couples physiological processes including photosynthesis, stomatal conductance, plant hydraulics, and canopy radiative transfer, with an eco-evolutionary optimal allocation scheme for leaves and roots. This is applied to wheat by coupling it to a source-sink grain production module. Using a series of idealized sensitivity experiments, we show that the model reproduces well-documented patterns of plasticity in carbon allocation: diminishing returns on leaf investment as canopy closure and water limitation are approached, a shift in allocation toward roots under drier conditions, and a root:shoot response that depends jointly on soil moisture and the plant's existing root:leaf balance. Simulating a full growing season across a gradient of soil moisture levels reveals a threshold-like response in canopy development, biomass accumulation, and grain yield, with yield constrained by the same assimilate supply that governs vegetative growth during the critical period and grain filling. This provides a promising basis for generalizing model behaviour across a wide range of environmental conditions. Evaluating these results against experimental and field trial data remains an important next step.

Auxins are a family of plant hormones involved in various processes across plant tissues and species. The Nuclear Auxin Pathway (NAP) consists of interacting transcription factors (ARFs) and repressors (Aux/IAAs), which govern an individual cells response to changes in auxin concentration. These components are present in all land plants, and many species possess multiple copies of each signalling component. We present a general framework for ODE-based models of NAP submodules with the flexibility to model the promotion and repression of target genes by any combination of transcriptional regulators. We analyse published data and show that auxin treatment in Arabidopsis thaliana roots triggers a range of characteristically distinct temporal response profiles--for both target genes and the signalling components themselves. Using our modelling framework, we recapitulate aspects of this behaviour by presenting examples of real and theoretical NAP subnetworks, and by analysing the effect that these network dynamics have on auxin-mediated transcriptional responses. This work demonstrates the utility of our modelling framework as a general-purpose tool for understanding the function of certain protein-protein and protein-DNA interactions through their effects on the NAP. This exploration of the rich dynamics of more complex signalling pathways promises to advance our understanding of the NAP.

Chlorophyll fluorescence imaging is a powerful tool used to characterize abiotic and biotic stress response of photosynthesis in plants. However, extracting metrics such as Fv/Fm (maximum quantum yield of photosystem II) from chlorophyll fluorescence images remains a substantial bottleneck in large measurement campaigns, often requiring significant manual effort. To resolve this issue, we developed fvfmPy, a user-friendly program which enables rapid analysis of chlorophyll fluorescence images of arrays of leaf discs. fvfmPy uses algorithmic image segmentation to identify regions of interest and extract fluorescence metrics from tens or hundreds of leaf samples per image with little to no manual input. fvfmPy is developed in Python and includes a graphical user interface enabling intuitive parameter adjustment and real-time quality control checking. We additionally developed fvfmR, an R wrapper package for fvfmPy. Fv/Fm values extracted with fvfmPy were indistinguishable from those obtained using manual methods (r2 > 0.99) but required 94% less time to complete (13 s vs. 200 s per 100 measurements). fvfmPy and fvfmR were built to directly integrate with existing fluorescence software packages and can greatly increase throughput of fluorescence measurements to help address key challenges in agriculture and ecology.

Root hairs (RH) are excellent model systems for studying cell size and polarity since they elongate several hundred-fold their original size. Their tip growth is regulated by both intrinsic and environmental signals and is associated with the existence of a highly controlled cytoplasmic tip Ca{superscript 2} gradient, whose disruption impairs RH development. The molecular mechanisms underlying the Ca2+ homeostasis fine tuning and the Ca2+ organellar contributions to the cytoplasmic pool remain unclear. In the model plant Arabidopsis thaliana, many efflux routes are present, including those that employ Ca2+-pumps from the Autoinhibited Ca2+-ATPase (ACA) family. Here, we identified that the ER localized ACA2, and to a lower extent ACA7, are crucial ACAs required to control RH growth. By using genetically encoded Ca2+ biosensors we showed that Ca2+-dynamics are compromised in the aca2-2 mutant, having lower cytosolic Ca2+ concentration [Ca2+]cyt and growth rate, showing an altered homeostatic calcium setpoint compared to Col-0. Accordingly, the ACA2 mutation changed the dynamics of [Ca2+]cyt oscillations coupled to growth rate, inducing longer periods and more regular oscillations in the dominant high-frequency range (around 22 s), and slower oscillations (around 1 min) in the low-frequency range. Finally, expression of ACA2 with changes in four putative Ca2+ binding residues (ACA2{Delta}Ca2+) failed to rescue the RH growth phenotype in the aca2-2 mutant. Collectively, our findings indicate that ER-localized ACA2 and possibly ACA7 are crucial for modulating cytoplasmic Ca2+ signals, possibly composing a critical part of a negative feedback loop, and their absence leads to impairments in RH cell elongation.

Photosynthesis is among the most consequential yet genetically complex traits in crop plants, and translating its natural variation into actionable genomic targets remains a central challenge for breeding climate-resilient varieties. To start addressing this, researchers are generating increasingly large, multi-environment field photosynthesis datasets. Yet, these data have been structurally under-analysed since their inception. Here we report the outcomes of the first dedicated hackathon focused on computational mining of such field data held in Accra, Ghana, in March 2026. Bringing together data scientists, plant physiologists, geneticists, and breeders from Europe and Africa, these interdisciplinary teams used photosynthetic data collected with hand-held fluorometers to genome-wide marker data across four crop species: cowpea (Vigna unguiculata), barley (Hordeum vulgare), common bean (Phaseolus vulgaris), and potato (Solanum tuberosum). Despite using different species and methods, independent teams identified the same three key findings. First, mechanism-informed feature engineering and dynamic modelling recover genetic signals that are not detected or discarded in standard analysis pipelines, resulting in traits with improved heritability and meaningful associations with yield. Secondly, machine learning methods proved effective at uncovering genetic associations, with temporally resolved features substantially outperforming single time-point measurements. Third, raw chlorophyll fluorescence and absorbance traces consistently contained more information and predictive power than the extracted parameters currently used. A defining feature of this event was having experimentalists and data scientists working together, enabling AI approaches to be grounded in domain knowledge and biological mechanisms rather than relying on data alone.

1O_LIEmbolism, the formation of air bubbles in the plant water transport system, is a mechanistic driver of plant death. The Optical Vulnerability Technique (OVT) is an imaging method for non-invasive quantification of embolism (including P50, a common metric for drought vulnerability), which can also provide detailed spatial and temporal information. Its major cost lies in the post-processing of thousands of images. C_LIO_LIHere we designed, tested, trained, and make publicly available a neural network model to automate post-processing of OVT images. Using a dataset of 65 leaves from Senecio pterophorous, we compared our model predictions to results obtained via traditional post-processing by an expert. C_LIO_LIOur model resolved P50 to within 0.027 MPa of the expert-processed data with training taking 30 minutes to 2.5 hours and model-runtime in the order of seconds to minutes, demonstrating its promise for increasing the efficiency and throughput of P50 calculation. The models performance in replicating the pixels that constitute embolism events was lower (mean event-frame IoU of 0.38). C_LIO_LIWe invite the community to utilise our model but emphasise that it does not replace the expert-processing pipeline and that care must be taken when considering applying this and similar approaches to OVT data. C_LI

Gene regulatory networks (GRNs) establish the spatio-temporal gene expression patterns necessary for reproducible development of organisms. MicroRNAs have emerged as important regulators of gene expression controlling either target gene levels, spatial patterns or timing. These molecular functions may vary depending on how they are integrated into GRNs. Here we used the Arabidopsis leaf as a model to study the genetic and molecular interactions between the CUP-SHAPED COTYLEDON 2 (CUC2) transcription factor and its negative regulator, miR164A. Quantitative morphometric analyses showed that miR164A acts from early stages during leaf development regulating CUC2 expression dynamic rather than the spatial distribution of CUC2. At the molecular level, CUC2 directly activates MIR164A expression, forming a single negative feedback loop. Further, using a ratiometric approach, we identified a cis-regulatory element (CRE) sufficient to drive CUC2-dependent MIR164A expression at the leaf sinus. Ordinary differential equation (ODE) modelling suggests that CUC2-CRE may control the timely accumulation of CUC2. Accordingly, quantification of CUC2 protein accumulation dynamics using a spatio-temporal mean cartography strategy in CRISPR cis-regulatory variants at the MIR164A locus revealed that CUC2-dependent MIRA164A expression is required to maintain both the precise timing of CUC2 expression and CUC2 levels. Altogether, our work provides experimental evidence for a specific role of the CUC2-MIR164A module in shaping leaf development, highlighting the contribution of network topology to GRN functions.

All living organisms rely on the movement of ions across cell membranes as the fundamental physical basis of their internal energy and signaling, and plants are no exception. Plants perceive, integrate, and respond to environmental stimuli through electrical signals, classified as action, variation, and system potentials, that are coupled with calcium waves, reactive oxygen species, and hydraulic and hormonal changes to coordinate whole-organism responses despite the absence of a nervous system. Yet most studies characterize these signals using a single feature, such as amplitude or spike duration, in a single tissue, an approach that cannot establish how such signals correspond to the underlying ionic activity, mobility, and structural complexity of the signaling environment, or how this correspondence varies across organs. Here, we correlate plant bioelectrical signals with potential ionic energy flow using a multi-domain framework, combining discrete spike events, continuous waveform properties, spectral composition, and signal complexity applied to leaf, stem, and root recordings from tomato (Solanum lycopersicum) exposed to different stimulus. Electrical activity with increased stimulus strength, likely reflecting increased ionic flow, with the root showing the largest response. This suggests plant electrical signaling works as a distributed, ion-based information system, useful for stress monitoring and bio-inspired sensor design.

Background and aims: Perennial root systems develop over decades and exhibit continuous turnover of structural and absorptive components, yet most root architecture models treat woody roots as permanent and neglect root age structure. We developed an empirically constrained modelling framework that explicitly represents age-structured demography, growth regulation and dormancy in perennial root systems. Methods: We extended the functional-structural plant model CPlantBox to simulate perennial root development over decades by integrating age-dependent survival, secondary radial growth and climate-driven dormancy. The model was parameterised using unique three-dimensional root system data from young and mature grapevines, together with dendrochronological root age measurements, and coupled to root hydraulic properties. Key results: Empirical analyses revealed multi-cohort age distributions and substantial turnover of lignified structural roots, contradicting the assumption of a static woody scaffold. The model reproduced age distributions, order-specific survival probabilities and fine-to-structural root length ratios, and generated realistic, genotype-specific three-dimensional architectures. Simulated uptake profiles and hydraulic scenarios showed strong age- and architecture-driven effects. Conclusions: Explicitly representing woody root age distributions and age-dependent processes may fundamentally change predictions of perennial root system architecture and function. The framework advances root modelling beyond annual paradigms and provides a transferable tool for analysing long-term architectural and hydraulic consequences of genotype x climate interactions in perennial crops.

{middle dot} Comparing maize canopies is confounded by two problems: plants differ in the number of phytomers they produce, and phytomer size scales with that number. Position along the shoot is therefore an unreliable basis for comparison, confounding shape with size. {middle dot} We first position the floral transition (tassel initiation, T.I.) within shoot development, revealing it as the landmark that organizes a conserved canopy shape. This provides the foundation for aligning canopies that differ in phytomer number. {middle dot} Focusing on the leaf blade, we used functional data analysis on a maize panel across controlled and field environments to decompose variation in blade-length profiles, which was dominated (77%) by overall amplitude, identifying scaling as the major source of size variation. Because blades enlarge with each phytomer until the transition, later T.I. yields a larger maximum; this T.I.-linked component explains up to one-third of scaling variation, reinforced by a shared genetic association between T.I. and blade size with the flowering-time gene ZmCCT10. {middle dot} Regressing out this component leaves a residual that defines a new, heritable axis of intrinsic scaling, independent of flowering time. It distinguishes hyper- and hypo-scaling genotypes consistently across environments, offering a selection target decoupled from phenology.

Process-based tree growth models provide a mechanistic framework for investigating how climate conditions regulate tree growth across daily to annual time scales. Yet, their broader application across species and environments is constrained by the limited accessibility in open-source environments and the difficulty of estimating physiological parameters that are rarely measured directly. Here, we present virtualRings, a new R package integrating the Vaganov-Shashkin model (VSM) and the RINGS3 models, and focus on the implementation and calibration of VSM. Using tree-ring width observations from seven Northern Hemisphere sites across various environmental conditions, we compared the traditional bootstrap-based calibration approach with the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). CMA-ES improved agreement between simulated and observed radial tree growth and provided an efficient approach for model parameter estimation. We further evaluated practical CMA-ES settings to balance computational cost and performance and discussed its potential limitations. The virtualRings package provides an open and reproducible platform for tree growth simulation, facilitating the application of important process-based models across species and environments and the investigation of how temperature and moisture constraints regulate daily tree-ring formation across spatial and temporal scales.

Shoot branching is a primary determinant of plant form and crop yield, yet whether auxin or carbon supply is the proximal regulator of branching remains contested. In Arabidopsis, an earlier report that exogenous auxin fails to restore apical dominance after decapitation has been taken to weaken the case for auxin, and several studies have proposed that sugars are the primary regulator. Resolving this has been difficult because most perturbations of sugar status also disturb auxin. Here, we revisit the control of Arabidopsis branching using well-controlled, largely unperturbed plants and approaches designed to isolate each pathway. Contrary to the earlier report, apically applied auxin restored the suppression of rosette branching after decapitation, placing Arabidopsis in line with other species. In a dataset of 718 plants, cauline and rosette branching were weakly but significantly negatively correlated, consistent with a polar-auxin-transport-based model and contrary to a previous conclusion of no relationship. Removing all rosette leaves at bolting slowed bud growth but did not alter the final number of branches. Varying photosynthetic photon flux density across six natural accessions, analyzed by piecewise structural equation modeling, showed that photoassimilate acted far more strongly on the mass deposited into branches than on whether a bud initiates a branch. We conclude that auxin remains a major regulator of apical dominance in Arabidopsis, and that photosynthetic assimilate, while required as a substrate for branch growth, contributes little to determining branch number but more to branch size and biomass.

Plants acclimate to low temperatures by remodeling carbohydrate metabolism. Permanent cold at 4/5{whitebullet} C induces well-characterized cold acclimation in plants, including accumulation of starch and soluble sugars. In natural environments, however, annual plants in their vegetative phase more frequently experience chilling nights (0-6{whitebullet} C nights followed by days at least 12{whitebullet} C warmer), whose metabolic consequences remain poorly understood. We investigated Col-0 Arabidopsis thaliana exposed to one or seven chilling nights and quantified central carbohydrate metabolites, starch, photosynthetic parameters, and maximal activities of enzymes linking sucrose synthesis and cleavage. We integrated these time-series data into a biologically constrained augmented neural ordinary differential equation (ANODE) model to infer diurnal reaction-rate dynamics. Chilling nights induced strong accumulation of sucrose, glucose, and fructose, particularly after seven nights. Both measured sucrose phosphate synthase (SPS) capacity and ANODE-predicted SPS rates increased. In contrast to permanent cold acclimation, daytime starch over-accumulation was absent. Thus, lacking starch over-accumulation together with increased SPS rates, chilling nights invoke distinct carbohydrate-partitioning patterns towards sucrose, indicating a fundamentally different acclimation strategy compared to well described permanent cold.

Understanding how root phenotypic plasticity enhances resource-use efficiency can help understand the outcomes of plant competition and identify suitable genotypes for medium- to low-input agricultural systems. Auxin regulates multiple root growth processes, including the root architectural responses to nitrogen (N). We explored the extent to which auxin-mediated local and systemic responses to external and internal N influences plant N uptake and use, using a functional-structural-plant (FSP) modelling approach. A simplified auxin module was developed at the level of the organ and integrated into an FSP model to represent physiological plastic root responses to N. Model performance was evaluated against experimental data. We then ran simulations under various N conditions with local or systemic responses enabled or disabled, to quantify their contribution to N uptake and use. Simulations showed that local auxin responses enhanced N uptake by distributing more roots towards deeper soil layers and increased N forage, thereby avoiding N stress. Systemic auxin responses reduced N uptake by distributing more roots near the top soil layers, which reduced plant size and N demand, thereby enhancing stress tolerance. This points to a trade-off in N uptake between the tolerance and avoidance strategies, which can be traced back to biomass and N investment resulting from source-sink relationships in the plant. Representing hormone-mediated root plasticity in an FSP model provides mechanistic insights into plant strategies for resource capture.

The xylem safety-efficiency trade-off is a central paradigm in plant hydraulics, yet empirical support remains inconsistent. In this study, we highlight how this inconsistency may arise from variation in environmental and evolutionary constraints and from differences in observational scale. We analysed 38 diffuse-porous angiosperm and 24 conifer species grown in a common garden experiment to quantify the relationship between embolism resistance (P50) and specific hydraulic conductivity (Ks) across and within species, and its differences between three species groups differing in evolutionary ancestry and eco-hydrological strategy. When pooling over all species, we found evidence for a safety-efficiency trade-off both across and within species. However, this pattern was driven almost exclusively by the mesic broadleaved species. In contrast, the across-species pattern was suppressed in coniferous species due to the evolutionary constraints on efficiency posed by their tracheid-based wood anatomy, while there was no evidence for a trade-off on either level in hydrophilic broadleaved species, which evolved under conditions that pose reduced selective pressure on embolism resistance. Our results demonstrate that the safety-efficiency trade-off is not universal, but emerges only in specific ecological and evolutionary contexts, highlighting the importance of multi-scale analyses and constraint-based frameworks for interpreting plant hydraulic strategies.

O_LIThe chloroplast CLP chaperone-protease is essential for chloroplast biogenesis. CLP substrate selection is aided by the N-recognin CLPS1 and CLPF adaptors. They interact with each other and the CLPC1 chaperone, but their specific functions are poorly understood. C_LIO_LIWe employed in vivo CLPC1 substrate-trapping in Arabidopsis by expressing a 35S:CLPC1-TRAP-STREPII transgene in wild-type (WT), clpf, clps1, and clpfclps1 to test the consequences of the loss of these adaptors on CLPC1-trapped proteins. Immunoblotting and protein half-life experiments were carried out for identified CLP substrates. C_LIO_LIExpression of the 35S:CLPC1-TRAP-STREPII in clps1cpf was embryo lethal. CLPF was trapped at a reduced level in clps1, supporting CLPS-CLPF interactions. Chloroplast 1O2 sensor EXECUTER1 (EX1) was trapped in WT and clps1 but not significantly in clpf. Steady-state protein accumulation of EX1 and its homolog EX2 increased 30-fold in the CLPC1-TRAP lines and clpr2-1, but not in clpf or clps1. In planta experiments showed that the half-life of EX1 is [~]3-fold longer in clpc1-1 than in WT, but EX1 half-life was unaffected in clpf. C_LIO_LIWe conclude that the CLP system plays a key role in EX1,2 homeostasis by keeping their intra-chloroplast concentrations low through continuous degradation, upstream of their 1O2 signaling function. C_LI

Water potential gradients drive water flow within and between soils and plants, and the internal plant water potential controls a wide range of physiological processes including photosynthesis, growth, and mortality. Notwithstanding this clear relevance for many critical aspects of ecosystem function, water potential data have historically been relatively inaccessible and unnetworked. The absence of a centralized repository for plant water potential time series limits our ability to integrate a wealth of ecophysiological information from other networks and from remote sensing. Closing this gap is necessary to address unresolved questions about plant responses to drought and heat stress, and to make confident predictions about plant and ecosystem function in a warming world. Here, we introduce the PSInet database -- a global collection of plant water potential time series from 285 datasets representing 523 species. We present the workflow that guided database development and evaluate its key features. Through a series of preliminary analyses, we then highlight the potential of the PSInet database for applications including: a) advancing plant water use strategy frameworks; b) disentangling the impacts of soil versus atmospheric drought stress; c) assessing the long-held assumption of pre-dawn equilibration of ecosystem water potential; d) understanding the risk of drought-driven mortality; and e) benchmarking remote-sensing data products and land-surface models.

Leaves of terrestrial plants possess heterogeneous anatomical structures composed of multiple cell layers. Although biochemical differences among leaf tissues have been inferred from protein analyses and anatomical studies, direct comparisons of dynamic photochemical responses among tissues while preserving their spatial context remain challenging. Leaves experience intrinsically heterogeneous environments because incident light enters primarily from above and propagates through complex internal leaf structures. Therefore, analyzing photosynthetic activity at the tissue and cellular levels is essential for understanding how photosynthesis operates within structurally heterogeneous leaves. Here, we combined live leaf-section imaging with microscopic Imaging-PAM chlorophyll fluorescence measurements to analyze photochemical responses at the tissue level in living leaf sections. In dorsiventral dicot leaves, palisade tissues exhibited a higher effective PSII quantum yield [Y(II)] and more rapid induction of regulated energy dissipation [Y(NPQ)] than spongy tissues, indicating higher photosynthetic capacity and photoprotective activity. In contrast, rice leaves, which lack palisade-spongy differentiation, showed uniform photochemical responses along the adaxial-abaxial axis. In the C4 plant finger millet, mesophyll and bundle sheath cells displayed distinct photochemical responses consistent with their functional differentiation in C4 photosynthesis. These results demonstrate that photosynthetic responses are spatially organized according to leaf anatomical structure. Although section-based measurements do not reproduce the native optical and gas environments of intact leaves, they enable the comparison of intrinsic tissue-specific photochemical properties under approximately equivalent illumination. The present approach enables tissue-resolved chlorophyll fluorescence analysis in living leaves, providing a new framework for investigating how leaf architecture shapes the spatial organization of photosynthetic activity.